Artificial General Intelligence System and Method for Medicine

ABSTRACT

A medical general intelligence computer system and computer-implemented methods analyze morpho-physiological numbers for determining a risk of an emergent disease state, determining an emergent disease state, predicting a pre-emergent disease state, determining a pre-emergent disease state, and/or predicting a risk of a pre-emergent disease state.

PRIORITY INFORMATION

This application claims the benefit of priority of U.S. ProvisionalPatent Application No. 61/651,797 filed May 25, 2012 and titled “MEDICALARTIFICIAL GENERAL INTELLIGENCE: MEDICAL REASONING SYSTEM (MRS),” whichis herein incorporated by reference in its entirety.

FIELD

This disclosure generally relates to an artificial intelligence systemapplied to the medical arts and a computerized method for making amedical diagnosis.

BACKGROUND

Biological systems are complex. Complex means that the data is nonlinearand/or dynamic. Generally, determining a risk of an emergence of adisease state (i.e., emergent disease state; a phenotype of a diseasestate being expressed) is an unsolvable problem in polynomial time,exponential time, or finite time. Further, generally, predicting apre-emergence of a disease state (i.e., pre-emergent disease state;prior to actual emergence of that disease state) is an unsolvableproblem in polynomial time, exponential time, or finite time.

SUMMARY

An embodiment of a computer-implemented method for determining a diseasestate comprises a processor accessing from a computer-readable memory,computer-readable data representing a general graph having nodes ofphysiological conditions, wherein each of the physiological conditionsis represented by each of the nodes, wherein each of the nodes isconnected by one or more edges representing correlations between thenodes, the processor accessing from the computer-readable memory,computer-readable data of physiological conditions of a patient, theprocessor executing computer-readable instructions for quantification ofthe physiological conditions of the patient, the processor transformingthe physiological conditions of the patient to node data by performingquantification of the physiological conditions of the patient, theprocessor executing computer-readable instructions for associating thenode data to the general graph, the processor creating an individualizedgraph by associating the node data to the general graph, the processorstoring the individualized graph to the computer-readable memory, theprocessor accessing from the computer-readable memory, computer-readabledata representing a topological module of nodes connected by edges,wherein the topological module represents the disease state, and theprocessor executing computer-readable instructions for mapping thetopological module to the individualized graph.

An embodiment of the computer-implemented method further comprises theprocessor mapping the topological module to the individualized graph,wherein the disease state is an emergent disease state.

An embodiment of the computer-implemented method further comprises theprocessor mapping the topological module to the individualized graph,wherein the disease state is a pre-emergent disease state.

An embodiment of a computer-implemented method for creating a graph fordetermining a disease state comprises storing on a computer-readablememory, computer-readable data representing a general graph ofphysiological conditions, wherein each of the physiological conditionsis represented as a node of the general graph, wherein each node isconnected by one or more edges representing correlations between thenodes, and storing on the computer-readable memory, computer-readabledata representing a topological module of nodes connected by edges,wherein the topological module represents the disease state, and thetopological module can be mapped on to a portion of the general graph.

An embodiment of a computer-implemented method for determining apre-emergent disease state comprises a processor accessing from acomputer-readable memory, computer-readable data representingphysiological conditions represented as nodes, wherein at least one ofthe nodes has a correlation to another one of the nodes, the processoraccessing from the computer-readable memory, computer-readable data offirst physiological conditions of a patient collected at a first timeperiod, the processor transforming the first physiological conditions ofthe patient to first node data by performing quantification of the firstphysiological conditions of the patient, the processor accessing fromthe computer-readable memory, computer-readable data of secondphysiological conditions of the patient collected at a second timeperiod, the processor transforming the second physiological conditionsof the patient to second node data by performing quantification of thesecond physiological conditions of the patient, the processor accessingfrom the computer-readable memory, computer-readable data representing atopological module of nodes, wherein the topological module representsthe pre-emergent disease state, the processor mapping the topologicalmodule to the first node data, the processor mapping the topologicalmodule to the second node data, and the processor determining anpre-emergent disease state based on the mapping the topological moduleto the first node data and to the second node data.

An embodiment of a medical general intelligence computer comprises aprocessor, and a computer-readable memory in communication with theprocessor, the computer-readable memory including and/or stored thereonone or more processor-executable instructions for the processor toperform one or more of the computer-implemented methods disclosedherein.

BRIEF DESCRIPTION OF THE DRAWINGS

Referring now to the drawings in which like reference numbers representcorresponding parts throughout.

FIG. 1 illustrates a box diagram of an embodiment of the MRS.

FIG. 2 illustrates an example of a graph which is a visualrepresentation of data stored on a memory of a MRS.

FIG. 3 illustrates an example of a complex relationship view of a graphbetween physiological features (e.g., nodes) and their connections(e.g., edges).

FIG. 4 illustrates an example of a visual representation of an analysisprocess carried out by the MRS.

FIG. 5 illustrates examples of topological modules.

FIGS. 6-8 illustrate an embodiment of the AGI method.

FIG. 9 illustrates an example of the numerical analysis of node valueswhich can be performed by the MRS.

FIG. 10 illustrates an example of the steps carried out by the MRS usingthe AGI method.

FIG. 11 illustrates an example of an emergent disease state vs.non-emergent disease state.

FIG. 12 illustrates an example of an individual's physiological datacollected along a time dimension.

FIG. 13 shows an example chart showing a change in the disease intensityalong a time dimension.

FIG. 14 illustrates an example of a chart resulting from the MRSperforming a metadata analysis of temporal data.

DETAILED DESCRIPTION

The embodiments disclosed are directed towards a computerized generalintelligence systems applicable to the medical arts and a computerizedmethod for determining risks associated with a medical diagnosis.

Generally, without a reproducible numerical descriptor, a particulardisease state is difficult to define, predict, and/or manage. Generally,useable medical data for defining, predicting, and/or managing a diseaseare numerical values associated with morpho-physiologic features. Suchnumerical values are defined herein as morpho-physiological numbers.Examples of morpho-physiological features are, for example but notlimited to, weight, heart rate, blood pressure, etc. Examples ofmorpho-physiological numbers are, for example but not limited to, 200lbs (weight), 50 beats per minute (heart rate), 160 mmHg (systole)/120mmHg (diastole) (blood pressure), etc.

The morpho-physiological numbers are different from data rising out of aperson's genetic makeup. That is, a person's genetic makeup (DNA) isgenerally a fixed micro-level blueprint which represents a geneticpotential of the person. The person's genetic makeup does not representthe person's current physiological state at a macro-level and real worldconditions. That is, it is impossible to determine whether the person iscurrently suffering from a disease state by looking only at the geneticmakeup of that person. The morpho-physiological features and numbers ofthe person must be determined and analyzed to determine whether theperson is currently suffering from a disease state (i.e., clinicaldescriptor).

Clinical descriptors such as “congestive heart failure”, “cancer”, etc.provide a name, or an identity, to a disease state. For example, aperson with a blood pressure numbers (morpho-physiological numbers) of160 mmHg (systole)/120 mmHg (diastole) can be determined to haveArterial Hypertension, i.e., high blood pressure (clinical descriptor).However, a disease state of a biological system (e.g., animal, human,etc.) is rarely a consequence of an abnormality in a singlephysiological feature. Instead, the disease state reflects perturbationsof a complex physiological network of linked morpho-physiologicalfeatures (e.g., tissues, organs, etc.). Thus, while the clinicaldescriptors are a convenient way of communicating in few words thecomplex perturbations of the physiological network, the clinicaldescriptors, by themselves, have not been able to provide sufficientinformation for numerical and/or computational analysis needed fordetermining risk levels and/or management levels of perturbations ofmany morpho-physiological features. That is, the clinical descriptorshave been considered useless for numerical evaluations because theclinical descriptors are not numbers. For example, the clinicaldescriptor of “cancer” (of a patient who is suffering from cancer) hasnot itself been directly used for numerical and/or computationalevaluation of the patient's current state of suffering from the disease,nor for determining risks of other related physiological conditionsand/or disease states that the patient may suffer from in the near ordistant future. That is to say, clinical descriptors (e.g., names ofdiseases) were conceived for the sake of easily communicating complexphysiological phenomenon between humans. Clinical descriptors have notbeen directly computable. Accordingly, clinical descriptors had not beenused in determining and/or predicting risk levels of these perturbationsof the morpho-physiological features. Additionally, clinical descriptorshad not been used in determining and/or predicting an emergent diseasestate and/or a pre-emergent disease state.

Disclosed herein are systems and methods that define clinicaldescriptors as a connection of morpho-physiological features, such thatthe clinical descriptors can be used in determining and/or predictingrisk levels of these perturbations of the morpho-physiological features.Disclosed herein are systems and methods that define clinicaldescriptors as a connection of morpho-physiological features, such thatthe clinical descriptors can be used in determining and/or predicting anemergent disease state and/or a pre-emergent disease state.

The systems and methods disclosed herein define clinical descriptors asquantifiable information. The systems and methods disclosed herein candescribe clinical descriptors as multi-dimensional computer-readabledata representing a topology of quantifiable morpho-physiologicalfeatures.

The topology is described herein as a topological module. Themorpho-physiological features are described herein as nodes and/or hubs.The morpho-physiological numbers are described herein as numericalvalues for the associated morpho-physiological features (nodes and/orhubs). Accordingly, the topological module is a network representationof connected nodes and/or hubs. Thus, clinical descriptors of diseasesare represented as computer-readable data represented as a networkconnections and/or relationships of numbers associated with nodes andhubs. Accordingly, clinical descriptors, and a disease state, can alsobe visually represented as a graph having a network of connected nodes,wherein at least a portion of the graph can have a topological module ofnodes connected by edges, wherein the topological module represents thedisease state. Therefore, according to the embodiments herein, clinicaldescriptors can be numerically analyzed and computed.

A Medical Reasoning System (MRS) is a specialized medical generalintelligence computer configured to perform the computer-implementedmethods disclosed herein. The MRS comprises a processor, and acomputer-readable memory in communication with the processor. Thecomputer-readable memory stores thereon one or more processor-executableinstructions for the processor to perform one or more of thecomputer-implemented methods disclosed herein.

FIG. 1 illustrates a box diagram of an embodiment of the MRS 100. TheMRS 100 is a specialized medical general intelligence computerconfigured to perform one or more of the computer-implemented methodsdisclosed above. The MRS 100 comprises a processor 102, and acomputer-readable memory 104 in communication with the processor 102.The computer-readable memory 104 stores thereon one or moreprocessor-executable instructions for the processor 102 to perform oneor more of the computer-implemented methods disclosed above. Thecomputer-readable memory 104 can store one or more of computer-readabledata, graph data, node data, node value, hub data, hub value, edge data,topological module data, individualized graph data, meta-data, andinference data. Examples of the computer-readable memory 104 arenon-transitory storage medium, such as but are not limited to, magneticmedia (disc, film, ribbon, etc.), optical media (disc, film, ribbon,etc.), ROM, RAM, flash media, solid state drive, etc. The MRS 100 isconfigured with one or more Input/Output (I/O) device(s) 106 which is incommunication with the processor 102. Examples of the I/O device(s) 106include but are not limited to a keyboard, a mouse, a touchscreen, adisplay device, etc. The I/O device(s) 106, such as the touchscreenand/or the display device, can display thereon a graphical userinterface (GUI) for a user to interact with the MRS 100. The GUIprovides for displaying of information to the user and can receive userinput via the user's interaction with the GUI.

The I/O device(s) 106 are device(s) configured to allow a user tointeract with the MRS 100 and/or for the MRS 100 to interact with theuser. Accordingly, one or more I/O device(s) 106 can provide forentering data (input), displaying data (output), displaying results ofthe analysis (output), etc.

An embodiment of the MRS 100 is configured to operate acomputerized-method including an Artificial General Intelligence (AGI)method. The MRS 100 can analyze a topological module andmorpho-physiological numbers for determining and/or predicting risklevels of perturbations of the morpho-physiological features. Further,the MRS 100 can analyze a topological module and morpho-physiologicalnumbers for determining a risk of an emergent disease state. Further,the MRS 100 can analyze a topological module and morpho-physiologicalnumbers for determining an emergent disease state. The MRS 100 cananalyze a topological module and morpho-physiological numbers forpredicting a pre-emergent disease state. Further, the MRS 100 cananalyze a topological module and morpho-physiological numbers fordetermining a pre-emergent disease state. Further, the MRS 100 cananalyze a topological module and morpho-physiological numbers forpredicting a risk of a pre-emergent disease state.

An embodiment of the methods performed by the MRS 100 includes a medicalapplication of an “artificial general intelligence (AGI)” method. TheAGI method described herein are directed to simplifying complex events.The AGI method can be applied to many different domains. Thus, the AGImethod is domain-independent. The AGI method does not attempt toreplicate and/or compute every feature of a complex event. In contrast,the AGI method takes the features of the complex event, and simplifiesthe complex event to few features that are determined to be essentialand/or important to that complex event. Accordingly, the AGI methodallows for extremely fast computation and/or analysis of complex events.

The computer-implemented method including the AGI can integrate an“autonomous learning,” which is described herein to mean that thecomputer system can perform analysis of data without supervision by aperson. The computer-implemented method including the AGI can integratea “goal-directed learning,” which is described herein to mean that thecomputer system can perform selective analysis of data towards a goalset by the computer, without supervision by a person. Thecomputer-implemented method including the AGI can integrate an “adaptivelearning,” which is described herein to mean that the computer systemcan perform cumulative and contextual analysis of data withoutsupervision by a person. Adaptive learning can lead to additional data(e.g., new data).

The computer-implemented method including the AGI can include patternrecognition and pattern matching (e.g., mapping). Accordingly, thecomputer-implemented method including the AGI can include determining aninference between one set of data (wherein “set” can be one or morevalue(s)) to another set of data. An example of the inference betweensets of data includes sets of data for same parameters taken atdifferent times. The AGI method understand the concept of thetime-dimension for these sets of data, and based on the changes in thesets of data, an inference data can be created as meta-data for thecollective sets of the data.

The MRS 100 configured to perform the medical application of the AGImethod can perform an analysis of data without the need to usedisease-dependent language. That is, the MRS 100 can use data havingdisease-independent language. The data used by the MRS 100 areformulated and associated in the terms and statements derived fromexperience-grounded semantics. That is, the formulations and theassociations of the terms are pre-determined and determined based onexperiences arising out of research (which a user inputs into and storedin the system memory) and/or self-learning (e.g., autonomous,goal-directed, and/or adaptive). An example of self-learning dataincludes but is not limited to inference rules of nodes determined bythe system based on the data stored in the memory.

The embodiments disclosed herein are also directed to the MRS 100 andmethods for performing an analysis (e.g., mapping) of one or moretopological modules stored in the memory in comparison to themorpho-physiological numbers of nodes and/or hubs, determining apre-emergent disease state (and/or risk(s) thereof). The embodiments canalso provide an output for suggested management of particularmorpho-physiological feature(s) for preventing an emergence of thepre-emergent disease state.

FIG. 2 illustrates an example of a graph 200 which is a visualrepresentation of data stored on a memory of a MRS. The graph 200 isformed of a plurality of nodes 202, 204, 206 connected via edges 208,210, 212, 214, 216, 218. Each edge 208, 210, 212, 214, 216, 218 connectstwo nodes. For example, edge 208 connects nodes 202 and 204. A node 202can have one edge 208 connected thereto. A node 206 can have multipleedges 210, 212, 214, 216, 218 connected thereto. A node 206 having morethan a set number of edges connected thereto can be determined to be ahub. For example, if the set number is 3, then the node 206 is a hub206. Each node 202, 204 and hub 206 of the graph 200 can have anumerical value associated with it. The graph 200 may be a generalgraph, which does not have numerical values (or the numerical values arenull values) associated with each of the nodes 202, 204 and hub 206.

The MRS includes a processor that can access the memory for building thegraph 200, modifying the graph 200 by adding additional nodes, hubs,and/or edges, removing nodes, hubs, and/or edges, etc. Suchmodifications can be done by a user interacting with the MRS and/orself-learning by the MRS by appropriate analysis of other data. Forexample, if a new research shows that an association between node 202and node 204 does not exist, then the processor of the MRS can accessthe memory and remove the edge 208 associating the nodes 202, 204. Thiscan be performed by the user purposefully interfacing with the MRS toremove the data representing the edge 208 of the graph 200, or the usercan enter the change in the relational information between the twofeatures that are represented as nodes 202, 204 and edge 208, and theprocessor of the MRS automatically, and dynamically, changes the datarepresenting the graph 200 accordingly (by removing the edge 208).Addition(s) of one or more of the nodes, hubs, and edges can also beperformed by the user purposefully interfacing with the MRS, or theprocessor of the MRS automatically, and dynamically, changing the datarepresenting the graph 200 according to new information the MRS hasaccess to.

Each of the nodes 202, 204, 206 represents a computer-readable data of aphysiological feature (e.g., condition of a person, animal, etc.). Eachof the edges 208, 210, 212, 214, 216, 218 represents a relationship(e.g., association, causation, inference, etc.) between twophysiological features. For example, in the medical domain ofcardiology, early diastolic velocity of the mitral valve annulus (e′)and a ratio E/e′ (wherein E is an early transmitral flow velocity) arerelated features. Thus, the node representing e′ and the noderepresenting E/e′ would have an edge connecting them together. Also, thenode representing E and the node representing E/e′ would have an edgeconnecting them together. Further, because E/e′ feature is related toleft ventricular diastolic pressure (LVDP), a node representing E/e′would have an edge connecting to a node representing LVDP.

The processor can create a set of data representative of anindividualized graph for a particular individual by populating the nodesof the graph 200 with numerical values representing the quantified(e.g., measured) physiological condition of the individual. Thenumerical values may be a scaled value (e.g., 1 to 4), wherein thescaled value is a translation of real measured value. The processor canstore the individualized graph to the memory of the MRS. For example,when the processor receives an individual's physiological data for e′and E, the processor can populate the respective nodes for e′, E, andE/e′ with the physiological data and/or scaled values representing thereceived physiological data based on a set ranges (stored in the memory)for the respective physiological features. Accordingly, the processorcan create the individualized graph for the particular individual bypopulating the nodes e′, E, and E/e′ with quantifiable numerical values.The processor may determine not to populate one or more of nodes of thegraph 200 with one or more of the received physiological data whencreating the individualized graph. Such determination can be made by theprocessor when the physiological data includes information for a nodethat represents a less important feature (e.g., has only one edgeconnected to another node (which is not a hub)).

FIG. 3 illustrates an example of a complex relationship 300 view of agraph (e.g., graph 200 shown in FIG. 1) between physiological features(e.g., nodes) and their connections (e.g., edges). The curve 302represents a power law relationship between the physiological features304 and their interconnectivities (i.e., connectivity) 306. The Y-axis308 represents the number of quantifiable physiological features 304.The X-axis 310 represents the number of connectivity 306 that existbetween the features. According to the power law curve 302, there arevery few quantifiable physiological features 304 what have highconnectivity 306. Dimensionally compressed view 312 of the curve 302shows that there are many features (nodes) 314 with few connections, andthat there are a small number of features (hubs) 316 with high number ofconnections.

FIG. 4 illustrates a visual representation 400 of an analysis process(e.g., mapping) carried out by the processor of the MRS, wherein aclinical descriptor has been transformed into a topological module 402.

The MRS can represent one or more clinical descriptors (diseases) asquantifiable association of physiological features and quantifiabledata. That is, the MRS can represent clinical descriptors of diseasesare represented as computer-readable data represented as a networkconnections and/or relationships of numbers associated with nodes andhubs. Accordingly, clinical descriptors, and a disease state, can alsobe visually represented as a topological module 402 having a network ofconnected nodes.

The topological module 402 has been mapped onto a graph 404 in thevisual representation 400. The mapping can be performed by patternrecognition and/or pattern matching method performed by the processor ofthe MRS. For the visual representation 400, the topological module 402is indicated by a border (e.g., surface created by a network of nodesand edges representing the topological module 402). The topologicalmodule 402 represents a group of nodes wherein their perturbations arevalidated to be linked to a particular disease. The topological module402 has nodes (features) that are considered to be necessary to specificdisease states. The MRS employing the AGI method can segregatenon-essential nodes and removes them from being consideration(computational analysis does not include non-essential nodes). Forexample, the topological module 402 includes a network of nodes that areless connected 406 (shown as open circles). The topological module 402includes nodes that are highly connected 408 (shown as closed circles).The MRS employing the AGI method can segregate the less connected nodes406 (shown as open circles) as non-essential nodes of the topologicalmodule 402 and need not consider them for further analysis of making amedical determination and/or prediction associated with the topologicalmodule 402 and an individual's physiological data. Only the highlyconnected nodes 408 (A, B, C, D, E) of the topological module 402 can beconsidered. The term highly connected means that the number of edgesconnected to a node is greater than a set number. In this example, theset number is four. Each of the highly connected nodes 408 (A, B, C, D,E) have number of edges connected thereto that is greater than four. Theset number can be changed as needed and/or desired by the MRS and/or auser.

The processor of the MRS can create a disease module 410 into memory,represented by the highly connected nodes 408 (A, B, C, D, E). However,the MRS need not create a separate module 410 from the topologicalmodule 402. Further analysis can be performed using the topologicalmodule 402, but considering only the highly connected nodes 408(ignoring the less connected nodes 406), instead of creating a separatedisease module 410 into memory. Accordingly, the terms topologicalmodule and disease module can be used interchangeably, wherein the termdisease module defines a subset of highly connected nodes of thetopological module of a disease state. Accordingly, the disease module410 can be viewed as a breakdown of the topological module 402.

Further, the highly connected nodes 408 can be defined to be a macronode. The macro node is used herein to mean a network of the fewestnumber of the most highly connected disease associated nodes.Accordingly, the macro node can represent a single quantifiable measureof a feature-to-disease expression. For each clinical descriptor, amacro node can be determined by the above process. The macro nodes canbe stored into the memory of the MRS. The macro nodes can be used in aninference process to compute the dynamical integrity (coherence,intensity, variance, etc.) of a particular emergent disease.

FIG. 5 illustrates examples of topological modules 502, 504 which can bemapped onto the graph 500. Each of the topological modules 502, 504represents a disease state (i.e., clinical descriptor). The MRS can mapone or more of the topological modules 502, 504 onto the graph 506. TheMRS can determine that at least a portion of the graph 506 representsone or more topological modules 508, 510 by, for example, patternmatching and/or pattern recognition. Accordingly, the topological module502 has the same network structure as the topological module 508 mappedonto graph 506, and the topological module 504 has the same networkstructure as the topological module 510 mapped onto graph 506. Whereas,the topological modules 502, 504 viewed separately does not necessaryconvey any information about their association, when mapped onto thegraph 506 as topological graphs 508, 510, it can be determined that thetopological graphs 508, 510 have overlapping nodes (A, B) 512, 514.Further, the topological modules 502, 504 viewed separately may notnecessarily convey the information that the nodes (A, B) 512, 514 areboth highly connected nodes, because each of the nodes 512, 514 in thetopological modules 502, 504 have only three edges connected thereto.However, when mapped onto the graph 506 as topological graphs 508, 510,it can be determined that the overlapping nodes (A, B) 512, 514 are bothhighly connected nodes (i.e., hubs) for both topological modules 508,510. Accordingly, by the mapping process, the MRS can determine thatthere is an inference relationship, represented by the overlapping nodes(A, B) 512, 514, between the two disease states represented by thetopological modules 502, 504.

FIGS. 6-8 illustrate an embodiment of the AGI method for simplifying acomplex network of physiological features to few highly connected hubsfor making an inference between two sets of topological modules.

FIG. 6 illustrates a visual representation 600 of a topological module602 of a disease state (e.g., emergent disease) mapped onto a graph 604.

FIG. 7 illustrates a visual representation 700 of the topological module602, wherein overlapping nodes 702, 704, 706, 708 (closed circles) ofthe topological module 602 and another topological module (402 shown inFIG. 4).

FIG. 8 illustrates a visual representation 800 of a portion of the graph(604 shown in FIGS. 6 and 7) showing only the inference nodes 702, 704,706, 708. These inference nodes 702, 704, 706, 708 can be defined to bea macro node 802 for the two topological modules (402 shown in FIGS. 4and 602 shown in FIG. 6). That is, the macro node 802 is a network ofthe fewest number of the most highly connected disease associated nodes702, 704, 706, 708 for both topological modules (402 shown in FIGS. 4and 602 shown in FIG. 6). Accordingly, the AGI method carried out by theMRS can determine that the same macro node 802 exists for the diseasestates represented as topological modules (402 shown in FIGS. 4 and 602shown in FIG. 6). This inference relationship data can be stored intothe memory of the MRS. The macro node 8002 and inference relationshipdata can be used in an inference process to determine an emergentdisease and pre-emergent disease. For example, if the topological module402 (shown in FIG. 4) is an emergent disease, and the topological module602 (shown in FIG. 6) is a pre-emergent disease, the inference nodesdetermined by the MRS allows the MRS to predict that the emergence ofthe disease represented by the topological module 402 (shown in FIG. 4)means that the disease represented by the topological module 602 (shownin FIG. 6) will or is highly likely to emerge. Numerical analysis of thenode values can be performed by the MRS to calculate the risks of theemergence of pre-emergent disease state represented by the topologicalmodule 602 (shown in FIG. 6).

FIG. 9 illustrates an example of the numerical analysis of node valueswhich can be performed by the MRS. The data/features contained within adisease module are typically nonlinear and vacillate among various stateweights. A disease is rarely a consequence or reflection of a singledata/feature but reflects the perturbations of a complex network thatlinks multiple data/features. Disease module behavior is not random buta series of general organizing principals in their structure andevolution. Data/feature nodes are interdependent and continuouslyreadjust in order to stabilize their integrated state.

FIG. 9 shows a chart 900 for performing a numerical analysis of valuesfor five nodes 902, 904, 906, 908, 910. For example, the nodes 902, 904,906, 908, 910 can be hubs of a topological module for a disease state.The topological module may be mapped onto a graph, wherein each of thenodes 902, 904, 906, 908, 910 has physiological data of an individualfor creating an individualized graph.

A scaled value is assigned by the MRS for the physiological data foreach of the nodes 902, 904, 906, 908, 910. That is, each node 902, 904,906, 908, 910 represents a group of highly connected physiologicalfeatures that represent a disease state, and physiological data has beenreceived by the MRS. The MRS scales the physiological data for each ofthe physiological features (nodes 902, 904, 906, 908, 910) and assignsthe scaled value to the nodes 902, 904, 906, 908, 910. The scaled valuescan be, for example but not limited to, integer values from 0 to 3,wherein 0=normal, 1=mild, 2=moderate, 3=severe. The scaled valuesrepresent a degree of perturbation for each of the physiological feature(node 902, 904, 906, 908, 910).

The chart 900 shows that the physiological data of the individual forthe physiological feature (node 902) is severely perturbed from normal(scaled value=3).

The chart 900 shows that the physiological data of the individual forthe physiological feature (node 904) is moderately perturbed from normal(scaled value=2).

The chart 900 shows that the physiological data of the individual forthe physiological feature (node 906) is mildly perturbed from normal(scaled value=1).

The chart 900 shows that the physiological data of the individual forthe physiological feature (node 908) is mildly perturbed from normal(scaled value=1).

The chart 900 shows that the physiological data of the individual forthe physiological feature (node 910) is severely perturbed from normal(scaled value=3).

The MRS can calculate a Total module weight 912 from the scaled valuesof the five nodes 902, 904, 906, 908, 910. For example, the Total moduleweight 912 can be a sum of the scaled values of the five nodes 902, 904,906, 908, 910, which is 10. A maximum modular weight value can bedetermined based on the number of nodes in the topological module andthe maximum scale value (in this example, 3) for each node. The maximummodular weight is a value represented by the maximum scale value (inthis example, 3) multiplied by the number of nodes (in this example, 5).Thus, in this example, the maximum modular weight is 3×5=15.

The MRS can calculate a Variance 914 of the individual's physiologicaldata from normal by, for example, dividing the Total module weight 912(value of 10) by a maximum modular weight (value of 15). This ratio isthe Variance 914. Accordingly, the Variance 914 can be a real numberscaled from 0.0 to 1.0, wherein 0.0 represents absolute normalcy, and1.0 represents severe variance from normal. Another form of numericalscale may be used. In this example, the Variance 914 is 10/15=0.67. Thatis, the physiological state of this individual has a Variance 914 fromnormal of 0.67. Accordingly, a single numerical value can be used inquantifying numerically and computationally determining perturbationsand variance of an individual's physiological state from normal withrespect to a clinical descriptor (disease state). In this example, theVariance 914 of 0.67 would indicate a “moderate” clinical state 916 inrelation to the disease state represented by the nodes 902, 904, 906,908, 910. An inference state 918 is a value determined by the maximumscaled value multiplied by the weight average value. The inference state918 is related to a probability of an event given the occurrence ofanother event. That is, the inference state 918 value can be used indetermining a quantifiable risk of emergence of another disease state.In this example, the maximum scaled value is 3, and the weight averagevalue is 0.67. Thus, the inference state 916 value is 3×0.67=2.1. Thesteps in the process shown in FIG. 9 and described in relation to FIG. 9are performed by the MRS.

FIG. 10 illustrates another example of the steps carried out by the MRSusing the AGI method.

In Step 1, the MRS performs a node aggregation step to a network ofnodes, reducing the total network (e.g., graph) using node aggregationinto a small singly connected graph 1000. The MRS identifies a macronode, or a set of fewest highly connected nodes 1002, 1004, 1006 (i.e.,hubs) in the graph 1000. The MRS can computationally represent thesehubs 1002, 1004, 1006 as an aggregated set of features (A, B, C) 1008forming the macro node 1010.

In Step 2, the MRS transforms the graph 1000 into a scaled value (i.e.numerical state) 1012 for each hub 1002, 1004, 1006, wherein each of thescaled value represents a clinically equivalent range for thephysiological feature represented by the hub 1002, 1004, 1006. Forexample, the scaled values can represent qualitative states 1014 ofnormal, mild, moderate, and severe.

In the example shown, the physiological features represented by nodes A1002 and C 1006 are determined by the MRS to be “moderate” and to havethe scaled values of 2. The physiological feature represented by node B1004 is determined by the MRS to be “severe” and to have the scaledvalue of 3.

In Step 3, the MRS determines that the number of hubs 1002, 1004, 1006defining the disease state (topological module) is 3 1016, the summedgroup state is 7 (i.e., 2+2+3) 1018, the maximum state of the diseasestate is 9 (i.e., physiological condition of an emergent disease state)1020, and the current condition value in relation to the disease stateis 0.78 (i.e., summed group state divided by the maximum state=7/9=0.78)1022. This current condition value infers the magnitude of variance fromnormal in relation to the disease state represented by the topologicalmodule defined by the hubs 1002, 1004, 1006.

Following are examples of hubs of a macro node which defines a diseasestate, and clinical ranges of the physiological data for the qualitativestates of normal, mild, moderate, and severe. The qualitative states ofnormal, mild, moderate, and severe can be assigned scaled values of 0,1, 2, and 3, respectively. The following examples and many othertopological modules can be stored in the memory of the MRS. The MRS canreceive patient data having physiological data for the followingfeatures, apply the AGI method described herein, and determine and/orpredict the related emergent disease state and/or pre-emergent diseasestate. The MRS can receive patient data having physiological data forthe following features, apply the AGI method described herein, anddetermine a risks for the related pre-emergent disease states frombecoming emergent.

Example 1: Endocrinology

Disease State: Diabetes Hub Normal Mild Moderate Severe Hgb A1c % <5.7to 6.4 ≥6.4 to 7.5  >7.5 to 8.5  >8.5 GFR ≥70 70 to 50 50 to 30 <30Glomerular filtration rate mL/min/1.73 m² Hemoglobin 13.8 to 17.2 M <1212 to 10 <10 gm/dL 12.1 to 15.1 F Weight (BMI) <25.0 25.0 29.9   30-39.9≥40

Example 2: Gastroenterology

Disease State: Celiac Disease Hub Normal Mild Moderate Severe TTGantibody Negative Variable Positive Test (anti- 10X abnormaltransglutaminase antibodies) EMA-IgA Negative Variable PositiveAnti-endomysial antibody HLA Genetic Negative Variable Positive TissueType (30% over (human diagnosis) leukocyte antigen Small Bowel NegatveSampling Error Positive Biopsy Variable

Example 3: Hepatology

Disease State: Light Chain Amyloidosis Hub NORMAL or ABSENT PRESENTSerum-Immune Fixation Yes/No Yes/No Urine-Immune Fixation Yes/No Yes/NoFree Immunoglobulin Light Chain >⅓ ratio >3 ratio Kappa/Lambda ratio

Example 4: Oncology

Disease State: Pancreatic Cancer Hub Normal Mild Moderate Severe CA 19-937-50 >100 (carbohydrate antigen 19-9) Cancer antigen 19-9 Chronic NoneMile Chronic Severe Epigastric Pain intermittent Jaundice None DocumentScreening CT Image of an None Positive Positive epigastric mass(Possible) (Definite)

Example 5: Pulmonology

Disease State: Obstructive Airway Hub Normal Mild Moderate SevereFEV1/FVC ratio <70% >70% Total Lung >80% 60-80 50-50 <50% CapacityResidual Volume ≤100 150 (RV/TLC ratio) DLC >80% <30% Diffusing LungCapacity

Example 6: Rheumatology

Disease State: Rheumatoid Arthritis Hub Normal Low Moderate High TenderJoints 0 28 Swollen Joints 0 28 Sedimentation 0-15 0-20 men Rate (mm/hr)0-20 0-30 fem C-Reactive <1.0 <3.0 Protein-hs mg/L Pain Scale (%) 0 100Global Patient 0 100 Scale (%) Global 0 100 Physician Scale (%)

FIG. 11 illustrates how the MRS determines and differentiates between anemergent disease state vs. non-emergent (or pre-emergent) disease state.In Status A 1100 and Status B 1102, the hubs (e′=myocardial relaxation;E/e′=filling pressure; LAVI=chronicity of filling pressure; E/A andDT=acuity of filling pressure) for heart failure state are provided. TheMRS reads the hubs 1104 from the memory of the MRS and processesphysiological data received by the MRS, and determines the quantitativeand qualitative state for each of the hubs 1104. In State A 1100, thequalitative states for the hubs 1104 are e′=moderate, E/e′=mild,LAVI=moderate, E/A=moderate, and DT=moderate. The MRS can perform theAGI method of quantitative determination of variance and conclude thatthe received physiological data in Status A 1100 is asymptomatic. Thatis, Status A 1100 is determined to have non-emergent disease for hearfailure.

In contrast, for Status B 1102, the MRS reads the hubs 1104 from thememory of the MRS and processes a different (either of anotherindividual from Status A, or from the same individual as Status A but ata different time) physiological data received by the MRS, and determinesthe quantitative and qualitative state for each of the hubs 1104. InState B 1102, the qualitative states for the hubs 1104 are e′=moderate,E/e′=severe, LAVI=severe, E/A=severe, and DT=severe. The MRS can performthe AGI method of quantitative determination of variance and concludethat the received physiological data in Status B 1102 has an emergentdisease, i.e., heart failure.

FIG. 12 illustrates an example of an individual's physiological data1200 collected along a time dimension (e.g., data has been collectedmany times at different times). The hubs 1202 (Left atrial volume indexLAVI, Tissue Doppler myocardial relaxation e′, Left ventricular fillingpressure E/e′, Mitrial valve deceleration time DT, Mitral valve MV)define the disease state of Atrial Fibrillation. At one point in time,the MRS has determined, based on the analysis of the values for the hubs1202, that the individual does not have an emergent Atrial Fibrillationdisease state. For example, the qualitative states of the hubs 1202 fortime 1204 are all normal.

Yet, at another point in time 1206, the MRS has determined, based on theanalysis of the values for the hubs 1202, that the individual does havean emergent Atrial Fibrillation disease state. For example, thequalitative states of the hubs 1202 for time 1206 are LAVI is abnormallylarge, e′ is abnormally low, E/e′ is highly variable, MV and DT are bothabnormally variable. The MRS can determine during the time period 1208that the individual is heading towards the emergent condition shown attime 1206. Periodic measurements of the physiological data for the hubs1202 during the time 1206 received by the MRS can be used in determiningthe total variance value (or disease intensity). As described above (forexample, FIG. 9), the disease intensity is a single numerical value (pertaking of a physiological data) which can be used in quantifyingnumerically and computationally determining perturbations and varianceof an individual's physiological state from normal with respect to adisease state. This disease intensity value can be measured at differenttimes, and the MRS can determine a trend in the change of the diseaseintensity value.

For example, FIG. 13 shows a chart 1300 showing a change in the diseaseintensity 1302 (Y-axis) along a time dimension 1304 (X-axis). The MRScan determine the intensity state 1302 of two disease modules (i.e.,chronicity 1306 and acuity 1308) over the time dimension 1304. The MRScalculates and stores disease intensity values of the disease modules1306, 1308. The MRS can infer a relationship between the two diseasemodules 1306, 1308 because the change in the data for the two diseasemodules along the time dimension shows an interdependence phenomena.Chronicity (historical burden of left ventricular filling pressure) andacuity (burden of filling pressure at the time of data acquisition) canbe drawn as a chart 1302 along the time dimension 1304 (the MRS cancomputationally determine this relationship without necessarily graphingthe chart 1304). The acuity 1308 evolves to a lower degree of intensityand chronicity to a higher degree of intensity. Based on these timedependent changes the MRS can provide an output via the I/O device(s)for a user so that the user can appreciate the interdependency of dataand make a more informed management decision.

FIG. 14 illustrates an example of a chart 1400 resulting from the MRSperforming a metadata analysis of temporal data, for the disease HeartFailure, using hubs e′, LAVI, E/e′, DT, and E/A. The MRS determines adisease intensity (or variance) from a numerical analysis using the AGImethod for an individual's physiological data in relation to a diseasemodule. The chart 1400 includes a disease intensity 1402 (Y-axis) havinga value of 0 (normal) to 1 (severe). The chart 1400 has the timedimension along the X-axis. Thus, a first disease intensity 1404 isdetermined by the MRS for a 1st exam date. A second disease intensity1406 is determined by the MRS for a 2nd exam date. A third diseaseintensity 1408 is determined by the MRS for a 3rd exam date. A fourthdisease intensity 1410 is determined by the MRS for a 4th exam date. Atable of the disease intensities are shown in the table below. Further,based on the differences between the disease intensities, the MRS candetermine qualitative information as to whether the individual isimproving or deteriorating in time.

1st exam 2nd exam 3rd exam 4th exam date date date date DiseaseIntensity 0.89 0.61 0.5 0.79 Change Beginning Improve ImproveDeteriorateAccordingly, the MRS metadata analysis is cable of exhibiting a robustexpressions of disease status and tracking of management decisions.

The MRS can predict, based on the quantified numerical analysis usingthe AGI method of processing hubs related to a topological module andemergent conditions, one or more events that are pre-emergent. Further,based on the predicted events, the MRS can provide an output via I/Odevice(s) a suggested physiological and/or medical management/treatmentfor preventing the emergence of the predicted event.

The table below provides some examples of the MRS making a determinationof an emergent condition, predicted event(s), and suggested managementoutput.

Hubs Emergent Predicted Suggested Management (features) = Condition =Event(s) = Output Blood Pressure + = Hypertensive = Stroke, Heart =Medical Management Cardiac Heart Disease Failure, Renal DysfunctionFailure, etc. Ejection = Systolic = Heart Failure, = Medical/SurgicalFraction + Cardiac Coronary Artery Management Systolic DysfunctionDisease myocyte excursion (fibrosis) + wall motion Myocyte = Diastolic =Stroke, = Medical Management relaxation + Cardiac Congestive elevationLV Dysfunction Heart Failure filling pressure Multivariable = Valve =Stroke, Heart = Medical/Surgical echo/Doppler Disease Failure, DeathManagement hemodynamic profile Myocardial = Atrial = Stroke, Heart =Medical/Surgical relaxation (e′) + Fibrillation Failure, DeathManagement chronicity of LV filling pressure

The MRS can proceed with autonomous learning based on the graph,topological modules, and physiological data received and stored in thememory of the MRS. For example, where the MRS performs variancedeterminations based on hubs of a particular topological module, the MRScan include one or more nodes (having fewer connections than the hubs)of the topological node in addition to the hubs. Further, the MRS cansubstitute one or more of the hubs with one or more of the nodes. If theresulting variance and other quantitative analysis values are the sameor substantially similar to the values determined by using only thehubs, the MRS stores that data, so that in certain situations when a setof physiological data is received by the MRS and the received data islacking towards a value related to one or more of the hubs, the MRS“knows” that another data value (if available) can be substituted and/orused in place of the missing hub value in making the determinationand/or prediction.

With regard to the foregoing description, it is to be understood thatchanges may be made in detail without departing from the scope of thepresent invention. It is intended that the specification and depictedembodiment to be considered exemplary only, with a true scope and spiritof the invention being indicated by the broad meaning of the claims.

1. A computer-implemented method for determining a disease state themethod comprising: a processor accessing from a computer-readablememory, computer-readable data representing a general graph having nodesof physiological conditions, wherein each of the physiologicalconditions is represented by each of the nodes, wherein each of thenodes is connected by one or more edges representing correlationsbetween the nodes; the processor accessing from the computer-readablememory, computer-readable data of physiological conditions of a patient;the processor executing computer-readable instructions forquantification of the physiological conditions of the patient; theprocessor transforming the physiological conditions of the patient tonode data by performing quantification of the physiological conditionsof the patient; the processor executing computer-readable instructionsfor associating the node data to the general graph; the processorcreating an individualized graph by associating the node data to thegeneral graph; the processor storing the individualized graph to thecomputer-readable memory; the processor accessing from thecomputer-readable memory, computer-readable data representing atopological module of nodes connected by edges, wherein the topologicalmodule represents the disease state; and the processor executingcomputer-readable instructions for mapping the topological module to theindividualized graph.
 2. The computer-implemented method as in claim 1,further comprising: the processor mapping the topological module to theindividualized graph, wherein the disease state is an emergent diseasestate.
 3. The computer-implemented method as in claim 1, furthercomprising: the processor mapping the topological module to theindividualized graph, wherein the disease state is an pre-emergentdisease state.
 4. A computer-implemented method for creating a graph fordetermining a disease state, the method comprising: storing on acomputer-readable memory, computer-readable data representing a generalgraph of physiological conditions, wherein each of the physiologicalconditions is represented as a node of the general graph, wherein eachnode is connected by one or more edges representing correlations betweenthe nodes; and storing on the computer-readable memory,computer-readable data representing a topological module of nodesconnected by edges, wherein the topological module represents thedisease state, and the topological module can be mapped on to a portionof the general graph.
 5. A computer-implemented method for determining apre-emergent disease state, the method comprising: a processor accessingfrom a computer-readable memory, computer-readable data representingphysiological conditions represented as nodes, wherein at least one ofthe nodes has a correlation to another one of the nodes; the processoraccessing from the computer-readable memory, computer-readable data offirst physiological conditions of a patient collected at a first timeperiod; the processor transforming the first physiological conditions ofthe patient to first node data by performing quantification of the firstphysiological conditions of the patient; the processor accessing fromthe computer-readable memory, computer-readable data of secondphysiological conditions of the patient collected at a second timeperiod; the processor transforming the second physiological conditionsof the patient to second node data by performing quantification of thesecond physiological conditions of the patient; the processor accessingfrom the computer-readable memory, computer-readable data representing atopological module of nodes, wherein the topological module representsthe pre-emergent disease state; the processor mapping the topologicalmodule to the first node data; the processor mapping the topologicalmodule to the second node data; and the processor determining thepre-emergent disease state based on the mapping the topological moduleto the first node data and to the second node data.
 6. (canceled)